Image processing allows for the comparison of a reference image against another image or multiple images in order to determine a "match" or correlation between the respective images. Accordingly, a variety of different image matching techniques have been employed to determine such a match or correlation between images.
One such image matching technique is known as object classification. The object classification technique operates by segmenting the original image into a series of discrete objects which are then measured using a variety of shape measurement identifications, such as shape dimensions and statistics, to identify each discrete object. Accordingly, each of the discrete objects are then classified into different categories by comparing the shape measurement identifications associated with each of the discrete objects against known shape measurement identifications of known reference objects. As such, the shape measurement identifications associated with each of the discrete objects are compared against known shape measurement identifications of known reference objects in order to determine a correlation or match between the images.
Another image matching technique utilized in determining a match between images is a process known as match filtering. Match filtering utilizes a pixel-by-pixel or image mask comparison of an area of interest associated with the proffered image against a corresponding interest area contained in the reference image. Accordingly, provided the area of interest associated with the proffered image matches the corresponding interest area of the reference image, via comparison, an area or pixel match between the images is accomplished and the images are considered to match.
Yet another technique utilizes a series of textual descriptors which are associated with different reference images. The textual descriptors describe the image with textual descriptions, such as shape (e.g., round), color (e.g., green), and item (e.g., ball). Accordingly, when a proffered image is received for comparison, the textual descriptor of the proffered image is compared against the textual descriptors associated with the reference images. As such, the textual descriptor associated with the respective images under comparison are compared to each other in order to determine a best match between the textual descriptions associated with each image, and therefore a match between the respective images.
Each of the aforementioned image matching techniques utilize different types of data or partial image data to describe the images under comparison, however, the aforementioned techniques typically may not utilize the actual full image data associated with the each image in order to perform image comparison. Accordingly, the aforementioned techniques do not provide for an optimal accurate image comparison as the aforementioned techniques are limited to the usage of only a small or fractional portion of the full representative image data for comparison purposes. As such, the aforementioned techniques often result in the matching of very different images as having a correlation to one another, due in part by the limited amount or type of data used in the image comparison process.
It is therefore desirable to provide an image matching technique which can generate and utilize image data which is substantially representative of the entire images under comparison.